规划研究中负荷预测误差建模的联合季节性ARMA方法

R. Hafen, N. Samaan, Y. Makarov, R. Diao, N. Lu
{"title":"规划研究中负荷预测误差建模的联合季节性ARMA方法","authors":"R. Hafen, N. Samaan, Y. Makarov, R. Diao, N. Lu","doi":"10.1109/TDC.2014.6863150","DOIUrl":null,"url":null,"abstract":"To make informed and robust decisions in the probabilistic power system operation and planning process, it is critical to conduct multiple simulations of the generated combinations of wind and load parameters and their forecast errors to handle the variability and uncertainty of these time series. In order for the simulation results to be trustworthy, the simulated series must preserve the salient statistical characteristics of the real series. In this paper, we analyze day-ahead load forecast error data from multiple balancing authority locations and characterize statistical properties such as mean, standard deviation, autocorrelation, correlation between series, time-of-day bias, and time-of-day autocorrelation. We then construct and validate a seasonal autoregressive moving average (ARMA) model to model these characteristics, and use the model to jointly simulate day-ahead load forecast error series for all BAs.","PeriodicalId":161074,"journal":{"name":"2014 IEEE PES T&D Conference and Exposition","volume":"70 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Joint seasonal ARMA approach for modeling of load forecast errors in planning studies\",\"authors\":\"R. Hafen, N. Samaan, Y. Makarov, R. Diao, N. Lu\",\"doi\":\"10.1109/TDC.2014.6863150\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To make informed and robust decisions in the probabilistic power system operation and planning process, it is critical to conduct multiple simulations of the generated combinations of wind and load parameters and their forecast errors to handle the variability and uncertainty of these time series. In order for the simulation results to be trustworthy, the simulated series must preserve the salient statistical characteristics of the real series. In this paper, we analyze day-ahead load forecast error data from multiple balancing authority locations and characterize statistical properties such as mean, standard deviation, autocorrelation, correlation between series, time-of-day bias, and time-of-day autocorrelation. We then construct and validate a seasonal autoregressive moving average (ARMA) model to model these characteristics, and use the model to jointly simulate day-ahead load forecast error series for all BAs.\",\"PeriodicalId\":161074,\"journal\":{\"name\":\"2014 IEEE PES T&D Conference and Exposition\",\"volume\":\"70 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-04-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE PES T&D Conference and Exposition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TDC.2014.6863150\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE PES T&D Conference and Exposition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TDC.2014.6863150","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

摘要

为了在概率电力系统运行和规划过程中做出明智和稳健的决策,对产生的风负荷参数组合及其预测误差进行多次模拟,以处理这些时间序列的可变性和不确定性至关重要。为了使模拟结果可信,模拟序列必须保持真实序列的显著统计特征。在本文中,我们分析了来自多个平衡机构位置的日前负荷预测误差数据,并描述了诸如平均值、标准差、自相关、序列间相关、时间偏差和时间自相关等统计特性。然后,我们构建并验证了一个季节性自回归移动平均(ARMA)模型来模拟这些特征,并使用该模型联合模拟所有BAs的日前负荷预测误差序列。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Joint seasonal ARMA approach for modeling of load forecast errors in planning studies
To make informed and robust decisions in the probabilistic power system operation and planning process, it is critical to conduct multiple simulations of the generated combinations of wind and load parameters and their forecast errors to handle the variability and uncertainty of these time series. In order for the simulation results to be trustworthy, the simulated series must preserve the salient statistical characteristics of the real series. In this paper, we analyze day-ahead load forecast error data from multiple balancing authority locations and characterize statistical properties such as mean, standard deviation, autocorrelation, correlation between series, time-of-day bias, and time-of-day autocorrelation. We then construct and validate a seasonal autoregressive moving average (ARMA) model to model these characteristics, and use the model to jointly simulate day-ahead load forecast error series for all BAs.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信